NDLI: Graph Pattern Matching: Do We Have to Reinvent the Wheel?

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Graph Pattern Matching: Do We Have to Reinvent the Wheel?

Content Provider	ACM Digital Library
Author	Then, Manuel Gubichev, Andrey
Abstract	This paper presents an empirical study of how a wide spectrum of systems handle the graph pattern matching problem. Our approach is to take the well-known LUBM benchmark, model it across various domains (relational, RDF, property graph), and execute the benchmark queries on the corresponding systems. We evaluate the systems using a large data instance on a single machine (the largest dataset is LUBM-8000, which contains over 1 billion RDF triples). Additionally, we provide a brief analysis of how different cases of graph pattern matching problem are stressed by the benchmark queries. Our main finding is that, contrary to popular belief and various vendors' claims, modern native graph stores do not necessarily offer a competitive advantage over traditional relational and RDF stores, even for the graph-specific problem of pattern matching. To the best of our knowledge, this is the first independent empirical comparison of different approaches towards pattern matching performed on a large scale graph.
Starting Page	1
Ending Page	7
Page Count	7
File Format	PDF
ISBN	9781450329828
DOI	10.1145/2621934.2621944
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2014-06-22
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Rdf Benchmarking Graph-structured data
Content Type	Text
Resource Type	Article

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